Roundup 11/16/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours.


— Kinetica and NVIDIA offer a webinar this Thursday on GPUs in real-time computing. (h/t Oliver Vagner)

Good Reads

— In The Wall Street Journal, Burton Malkiel reviews Virtual Competition, a book about the darker side of the algorithmic economy. The book describes the use of algorithms to support price discrimination and collusion; it closes with a discussion of potential approaches to regulation.


— IBM’s James Kobelius lists stuff that deep learning can do.


— Researchers at Numenta publish a paper that summarizes results of a study that compares the performance of an unsupervised neural net with four conventional methods for sequence learning. The four baseline methods are ARIMA, Extreme Learning Machine (ELM), Long-Short Term Memory (LTSM), and Echo State Networks (ESN). Trigger alert: there is math. Spoiler: Numenta’s method does well, which is why they published the article.

Methods and Techniques

— Martin Heller explains how to get started with TensorFlow.

— In O’Reilly Radar, Valliappa Lakshmanan explains how to partition datasets for machine learning with BigQuery.


— BigML announces its fall release and offers a webinar on November 29 to review the new features.

— Scott Carey explains how Uber delivers machine-learning-as-a-service to the whole organization.

— NVIDIA partners with Microsoft to optimize its GPU development tools for Microsoft Cognitive Toolkit. Press release here.


— Moor Insights & Strategy releases two white papers pertinent to machine learning. The first details AMD’s Radeon Open Compute Platform (ROCm), which supports GPUs in HPC and DL. The second explains Xilinx’ reconfigurable acceleration stack, an FPGA-based approach for machine learning workloads.

— In Forbes, Moor’s Karl Freund says AMD’s ROCm could be a game-changer.

— HPC Wire releases its 48th annual TOP500 list of the world’s most powerful supercomputers. (h/t Bob Muenchen)

— In a video from the Intel HPC Developer Conference, Elmoustapha Ould-ahmed-vall — yes, that Elmoustapha Ould-ahmed-vall — explains performance optimization for deep learning frameworks running on Intel architecture.

— NVIDIA touts the energy efficiency of its DGX SATURNV supercomputer.


— In The Huffington Post, Sebastian Raschka discusses the impact of machine learning on healthcare. And Ben Newton of Sumo Logic describes how retailers use machine learning to predict demand on Black Friday.

— MIT Technology Review reports on a machine learning algorithm that can determine whether U.S. State Department secrets are properly classified. Too late for HRC, though.

— Insilico Medicine launches Aging.AI 2.0, a blood biochemistry predictor of human age the company developed with deep learning.


— GE buys and Bit Stew Systems, as it builds out its Predix platform for industrial-strength machine learning.

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